Molecular dynamics simulation of bimodal atomic force microscopy

Accurate Wide-Modulus-Range Nanomechanical Mapping of Ultrathin Interfaces with Bimodal Atomic Force Microscopy

The nanoscale determination of the mechanical properties of interfaces is of paramount relevance in materials science and cell biology. Bimodal atomic force microscopy (AFM) is arguably the most advanced nanoscale method for mapping the elastic modulus of interfaces. Simulations, theory, and experiments have validated bimodal AFM measurements on thick samples (from micrometer to millimeter). However, the bottom-effect artifact, this is, the influence of the rigid support on the determination of the Young's modulus, questions its accuracy for ultrathin materials and interfaces (1-15 nm). Here we develop a bottom-effect correction method that yields the intrinsic Young's modulus value of a material independent of its thickness. Experiments and numerical simulations validate the accuracy of the method for a wide range of materials (1 MPa to 100 GPa). Otherwise, the Young's modulus of an ultrathin material might be overestimated by a 10-fold factor.

Reducing molecular simulation time for AFM images based on super-resolution methods

Atomic force microscopy (AFM) has been an important tool for nanoscale imaging and characterization with atomic and subatomic resolution. Theoretical investigations are getting highly important for the interpretation of AFM images. Researchers have used molecular simulation to examine the AFM imaging mechanism. With a recent flurry of researches applying machine learning to AFM, AFM images obtained from molecular simulation have also been used as training data. However, the simulation is incredibly time consuming. In this paper, we apply super-resolution methods, including compressed sensing and deep learning methods, to reconstruct simulated images and to reduce simulation time. Several molecular simulation energy maps under different conditions are presented to demonstrate the performance of reconstruction algorithms. Through the analysis of reconstructed results, we find that both presented algorithms could complete the reconstruction with good quality and greatly reduce simulation time. Moreover, the super-resolution methods can be used to speed up the generation of training data and vary simulation resolution for AFM machine learning.